Modeling of functional brain imaging data

1999 ◽  
Author(s):  
Barry Horwitz
Keyword(s):  
Brain Imaging ◽  
Functional Brain Imaging ◽  
Imaging Data ◽  
Functional Brain ◽  
Brain Imaging Data

scholarly journals Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia

2019 ◽  
Vol 13 ◽  
Author(s):  
Philippe Albouy ◽  
Anne Caclin ◽  
Sam V. Norman-Haignere ◽  
Yohana Lévêque ◽  
Isabelle Peretz ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Developmental Disorders ◽  
Functional Brain Imaging ◽  
Imaging Data ◽  
Functional Brain ◽  
Congenital Amusia ◽  
Brain Imaging Data

scholarly journals The neuronal correlates of intranasal trigeminal function—an ALE meta-analysis of human functional brain imaging data

2010 ◽  
Vol 62 (2) ◽  
pp. 183-196 ◽  
Author(s):  
Jessica Albrecht ◽  
Rainer Kopietz ◽  
Johannes Frasnelli ◽  
Martin Wiesmann ◽  
Thomas Hummel ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Meta Analysis ◽  
Functional Brain Imaging ◽  
Imaging Data ◽  
Functional Brain ◽  
Brain Imaging Data

Connecting natural and artificial neural networks in functional brain imaging using structured sparsity

2018 ◽  
Author(s):  
Christopher R. Cox ◽  
Timothy T. Rogers
Keyword(s):  
Neural Networks ◽  
Brain Imaging ◽  
Synthetic Data ◽  
Functional Brain Imaging ◽  
Structured Sparsity ◽  
Imaging Data ◽  
Neural Network Models ◽  
New Approach ◽  
Functional Brain ◽  
Artificial Neural

ABSTRACTArtificial neural network models have long proven useful for understanding healthy, disordered, and developing cognition, but this work has often proceeded with little connection to functional brain imaging. We consider how analysis of functional brain imaging data is best approached if the representational assumptions embodied by neural networks are valid. Using a simple model to generate synthetic data, we show that four contemporary methods each have critical and complementary blind-spots for detecting distributed signal. The pattern suggests a new approach based on structured sparsity that, in simulation, retains the strengths of each method while avoiding its weaknesses. When applied to functional magnetic resonance imaging data the new approach reveals extensive distributed signal missed by the other methods, suggesting radically different conclusions about how brains encode cognitive information in the well-studied domain of visual face perception.

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